https://github.com/csyhuang/topicblob

Text to Topics

https://github.com/csyhuang/topicblob

Science Score: 10.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
  • .zenodo.json file
  • DOI references
  • Academic publication links
  • Committers with academic emails
    1 of 3 committers (33.3%) from academic institutions
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (6.9%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

Text to Topics

Basic Info
  • Host: GitHub
  • Owner: csyhuang
  • License: apache-2.0
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 28.3 KB
Statistics
  • Stars: 0
  • Watchers: 0
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Fork of banjtheman/TopicBlob
Created almost 6 years ago · Last pushed almost 6 years ago
Metadata Files
Readme License

README.md

TopicBlob: Simplified Topic Modeling

TopicBlob is a Python 3 library for processing textual data. It provides a simple API for diving into common natural language processing (NLP) taks around topic modeling such as finding similar documents and provide a list of topics givne input text.

``` from topicblob import TopicBlob

text1 = "The titular threat of The Blob has always struck me as the ultimate moviemonster: an insatiably hungry, amoeba-like mass able to penetrate virtually any safeguard, capable of as a doomed doctor chillingly describes it assimilating flesh on contact. Snide comparisons to gelatin be damned, it's a concept with the most devastating of potential consequences, not unlike the grey goo scenario proposed by technological theorists fearful of artificial intelligence run rampant."

text2 = "Myeloid derived suppressor cells (MDSC) are immature myeloid cells with immunosuppressive activity. They accumulate in tumor-bearing mice and humans with different types of cancer, including hepatocellular carcinoma (HCC)."


docs = [text1, text2]


tb = TopicBlob(docs, 5, 5)
tb.topics # {"['able', 'grey', 'capable', 'gelatin', 'titular']": 0, "['cells', 'myeloid', 'immunosuppressive', 'suppressor', 'hepatocellular']": 1}

tb.sims # {0: 1.0, 1: 0.0} TODO: get better examples

```

TopicBlob leverages NLTK and gensim , for the heavy lifting

Features

  • Topic Extraction
  • Similarity Search
  • BM25 search ( word ranking search)
  • Topic Search

Get it now

#TODO push to pip
$ git clone https://github.com/banjtheman/TopicBlob/
$ pip install --editable . 

Requirements

  • Python >= 3.5

Owner

  • Name: Clare S. Y. Huang
  • Login: csyhuang
  • Kind: user

Data Scientist. Climate Scientist. Ph.D in Geophysical Sciences (U of Chicago). Love coding, writing and playing music.

GitHub Events

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Last synced: over 2 years ago

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  • Total Commits: 13
  • Total Committers: 3
  • Avg Commits per committer: 4.333
  • Development Distribution Score (DDS): 0.308
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  • Avg Commits per committer: 0.0
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Banjo b****n@g****m 9
unknown d****1@g****m 2
csyhuang c****g@u****u 2
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